Evaluation of two public genome references for chinese hamster ovary cells in the context of rna‐seq based gene expression analysis

Abstract

RNA-Seq is a powerful transcriptomics tool for mammalian cell culture process development. Successful RNA-Seq data analysis requires a high quality reference for read mapping and gene expression quantification. Currently, there are two public genome references for Chinese hamster ovary (CHO) cells, the predominant mammalian cell line in the biopharmaceutical industry. In this study, we compared these two references by analyzing 60 RNA-Seq samples from a variety of CHO cell culture conditions. Among the 20,891 common genes in both references, we observed that 31.5% have more than 7.1% quantification differences, implying gene definition differences in the two references. We propose a framework to quantify this difference using two metrics, Consistency and Stringency, which account for the average quantification difference between the two references over all samples, and the sample-specific effect on the quantification result, respectively. These two metrics can be used to identify potential genes for future gene model improvement and to understand the reliability of differentially expressed genes identified by RNA-Seq data analysis. Before a more comprehensive genome reference for CHO cells emerges, the strategy proposed in this study can enable more robust transcriptome analysis from CHO cell RNA-Seq data. Biotechnol. Bioeng. 2017;114: 1603-1613. © 2017 Wiley Periodicals, Inc.

Control of amino acid transport into Chinese hamster ovary cells

“…Fragments per kilobase per million reads (FPKM) values were computed with RNA‐Seq by expectation maximization (RSEM) (Li & Dewey, ) after using Bowtie2 (Langmead & Salzberg, ) version 2.2.8 to align to the reference transcriptome. As a recent study has reported that use of different reference CHO genomes for read mapping may yield differences in gene expression quantification for the same sample (Chen, Le, & Goudar, ), we also utilized an alternative CHO genome reference for read mapping (CriGri_1.0; GenBank accession: GCA_000223135.1) to check AAT gene expression quantification. Although fewer AAT genes were counted using the CriGri genome (54/56), AAT gene expression quantification was very similar using both references (data not shown).…”

“…Culture aliquots containing 5 × 10 6 cells were then transferred to 50-ml vented TubeSpin Bioreactors, recovered by centrifugation, and cultured as described above in conditioned (Days 5-6) culture medium containing solubilized inhibitors. (Chen, Le, & Goudar, 2017), we also utilized an alternative CHO genome reference for read mapping (CriGri_1.0; GenBank accession:…”

Combating viral contaminants in CHO cells by engineering innate immunity

“…Understanding the role of type I IFN-mediated innate immune responses in CHO cells could be invaluable for developing effective virus-resistant CHO bioprocesses. Fortunately, recent genome sequencing 15–17 and RNA-Seq tools have enabled the analysis of complicated cellular processes in CHO cells 18,19 , such as virus infection.…”

An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures

“…Earlier studies published in landmark publications have unanimously shown that various CHO cell lines, such as CHO‐K1, CHO‐S, and DG44, differ quite remarkably in their geno‐ and phenotype, as CHO cell lines are highly variable and undergo hundreds of thousands of unique mutations over generations . Such a group of related organisms when exposed to the same high mutational environment are formally called as “quasispecies.” Similarly, CHO cell lines, that have a common ancestral background can be considered as a classic example of “quasispecies” on account of number of differences in their genotype.…”